Pre-clinical research advances, driven by technology breakthroughs, are changing the approach to discover methods aimed at treating human disease. Because of these advances, there is greater urgency for effective integration of the growing number of disparate data sources. Access to and use of disease model information is currently an inefficient process owing, at least in part, to the large number and complex nature of existing disease models and associated data. Research data generated from use of animal models constitute part of the decisional data package used to determine when and if a basic science discovery should be considered as a potential therapy and accelerated through the developmental process-translational science. The absence of such decisional data or, just as importantly, the use of inaccurate data contributes to a costly "slowdown" in many therapeutic development efforts. Optimizing this process for translational research is expected to enhance the path to new and effective cures. The proper modeling of human disease requires an understanding of how conditions in nonhuman species relate to human conditions. In very few cases are the conditions produced in animals equivalent to the human condition;it is more common for animal models to present one or more features that have relevance to a particular aspect of the human disease. In some cases, this relevance to a human condition is relatively straightforward;in others, it is quite complex. And it is not surprising-but is currently relatively uncommon-to find that models data generated during studies of one disease are also relevant to understanding another seemingly unrelated disease. A system that enables the efficient capture of these cross-over bits of information would be valuable to any therapeutic development process. Therefore, an important motivation for developing better access to resources for animal models of human disease is the enhanced ability to search across multiple animal models as well as to capture specific model information that may be relevant to multiple diseases. More rigorous methods for describing animal models will allow researchers to identify and examine commonalities and differences across multiple animal models. As more animal models are generated, it is important to develop better mechanisms by which these models are mapped onto human conditions as well as better ways to capture and then access this newly generated information. Such methods must eventually use automated ways of linking various types of representations to identify equivalent, comparable, or related concepts. Forward movement in developing a resource to address the issues discussed above will first require a means of identifying, locating and characterizing currently used access methods for all disease models, including their respective data repositories in a type of electronic directory of animal models of human disease. This directory should be built in an extensible fashion to accommodate future growth in content and functionality. The objective of this contract is to create a resource for integration and sharing of data and information about animal models. The project will consist of two components: The initial component of this project will address the development of the front end of the resource: specifically, a directory of available disease models. The envisioned directory would enhance information access and retrieval by assisting researchers to find information about animal models, and by supporting experts seeking to assist researchers to find such information. This resource directory would provide reference materials, information about the resources, and information about the animal models themselves. Initially this component would focus on a few model animal species (e.g., mouse, zebrafish), and eventually expand to other species and along other dimensions (e.g., microbes, tissues). Resource database development would focus on the needs of both the animal model and human disease research communities and the characteristics of existing resources, and address issues such as data description standards, user interface, user services, etc. The second component of this project of the planned initiative would take a knowledge-based approach to design a portal that provides a one-point-of-contact from which all models and data can be accessed and processed.